Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.6 KiB
Average record size in memory64.1 B

Variable types

Text1
Numeric6
Categorical1

Alerts

Credit_Score is highly overall correlated with Label_LoanDefaultHigh correlation
Label_LoanDefault is highly overall correlated with Credit_ScoreHigh correlation
CustomerID has unique valuesUnique

Reproduction

Analysis started2025-10-12 00:15:33.677835
Analysis finished2025-10-12 00:15:36.578344
Duration2.9 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

CustomerID
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2025-10-12T05:45:36.865223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowCUST00001
2nd rowCUST00002
3rd rowCUST00003
4th rowCUST00004
5th rowCUST00005
ValueCountFrequency (%)
cust000091
 
0.1%
cust010001
 
0.1%
cust000011
 
0.1%
cust000021
 
0.1%
cust000031
 
0.1%
cust000041
 
0.1%
cust000051
 
0.1%
cust000061
 
0.1%
cust009851
 
0.1%
cust009861
 
0.1%
Other values (990)990
99.0%
2025-10-12T05:45:37.169495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
02299
25.5%
C1000
11.1%
U1000
11.1%
S1000
11.1%
T1000
11.1%
1301
 
3.3%
2300
 
3.3%
3300
 
3.3%
4300
 
3.3%
5300
 
3.3%
Other values (4)1200
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)9000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02299
25.5%
C1000
11.1%
U1000
11.1%
S1000
11.1%
T1000
11.1%
1301
 
3.3%
2300
 
3.3%
3300
 
3.3%
4300
 
3.3%
5300
 
3.3%
Other values (4)1200
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02299
25.5%
C1000
11.1%
U1000
11.1%
S1000
11.1%
T1000
11.1%
1301
 
3.3%
2300
 
3.3%
3300
 
3.3%
4300
 
3.3%
5300
 
3.3%
Other values (4)1200
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02299
25.5%
C1000
11.1%
U1000
11.1%
S1000
11.1%
T1000
11.1%
1301
 
3.3%
2300
 
3.3%
3300
 
3.3%
4300
 
3.3%
5300
 
3.3%
Other values (4)1200
13.3%

Recency_Days
Real number (ℝ)

Distinct342
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.588
Minimum2
Maximum364
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-12T05:45:37.290245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile20
Q191
median185
Q3276.25
95-th percentile346.05
Maximum364
Range362
Interquartile range (IQR)185.25

Descriptive statistics

Standard deviation105.05834
Coefficient of variation (CV)0.5722506
Kurtosis-1.2085521
Mean183.588
Median Absolute Deviation (MAD)93
Skewness-0.030661018
Sum183588
Variance11037.256
MonotonicityNot monotonic
2025-10-12T05:45:37.396739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2698
 
0.8%
358
 
0.8%
1588
 
0.8%
1637
 
0.7%
3507
 
0.7%
2327
 
0.7%
2857
 
0.7%
446
 
0.6%
3416
 
0.6%
1696
 
0.6%
Other values (332)930
93.0%
ValueCountFrequency (%)
25
0.5%
34
0.4%
41
 
0.1%
52
 
0.2%
63
0.3%
72
 
0.2%
83
0.3%
94
0.4%
102
 
0.2%
111
 
0.1%
ValueCountFrequency (%)
3642
0.2%
3634
0.4%
3622
0.2%
3612
0.2%
3601
 
0.1%
3591
 
0.1%
3581
 
0.1%
3574
0.4%
3561
 
0.1%
3553
0.3%

Frequency
Real number (ℝ)

Distinct49
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.851
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-12T05:45:37.498480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median25
Q337
95-th percentile47
Maximum49
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.288841
Coefficient of variation (CV)0.57498051
Kurtosis-1.207634
Mean24.851
Median Absolute Deviation (MAD)12
Skewness0.025301161
Sum24851
Variance204.17097
MonotonicityNot monotonic
2025-10-12T05:45:37.596581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4232
 
3.2%
329
 
2.9%
1727
 
2.7%
4926
 
2.6%
3725
 
2.5%
125
 
2.5%
1225
 
2.5%
624
 
2.4%
2624
 
2.4%
2124
 
2.4%
Other values (39)739
73.9%
ValueCountFrequency (%)
125
2.5%
216
1.6%
329
2.9%
416
1.6%
518
1.8%
624
2.4%
720
2.0%
823
2.3%
919
1.9%
1019
1.9%
ValueCountFrequency (%)
4926
2.6%
4819
1.9%
4717
1.7%
4622
2.2%
4517
1.7%
4423
2.3%
4320
2.0%
4232
3.2%
4118
1.8%
4014
1.4%

Monetary_Value
Real number (ℝ)

Distinct949
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5084.863
Minimum105
Maximum9994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-12T05:45:37.696368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile713.55
Q12642.25
median4968
Q37572.25
95-th percentile9561.4
Maximum9994
Range9889
Interquartile range (IQR)4930

Descriptive statistics

Standard deviation2860.7018
Coefficient of variation (CV)0.56259171
Kurtosis-1.2056201
Mean5084.863
Median Absolute Deviation (MAD)2518.5
Skewness0.038142394
Sum5084863
Variance8183614.6
MonotonicityNot monotonic
2025-10-12T05:45:37.798535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46713
 
0.3%
82483
 
0.3%
83172
 
0.2%
91652
 
0.2%
45992
 
0.2%
40352
 
0.2%
29672
 
0.2%
18042
 
0.2%
32722
 
0.2%
87242
 
0.2%
Other values (939)978
97.8%
ValueCountFrequency (%)
1051
0.1%
1101
0.1%
1121
0.1%
1451
0.1%
1461
0.1%
1541
0.1%
1631
0.1%
1741
0.1%
1931
0.1%
2711
0.1%
ValueCountFrequency (%)
99941
0.1%
99861
0.1%
99672
0.2%
99481
0.1%
99361
0.1%
99241
0.1%
99211
0.1%
99181
0.1%
99081
0.1%
99041
0.1%

Loan_Amount
Real number (ℝ)

Distinct993
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25305.592
Minimum528
Maximum49961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-12T05:45:37.894361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum528
5-th percentile3704.95
Q112462.25
median25235
Q337586
95-th percentile47830.1
Maximum49961
Range49433
Interquartile range (IQR)25123.75

Descriptive statistics

Standard deviation14157.721
Coefficient of variation (CV)0.55947008
Kurtosis-1.2027302
Mean25305.592
Median Absolute Deviation (MAD)12499.5
Skewness0.026866855
Sum25305592
Variance2.0044108 × 108
MonotonicityNot monotonic
2025-10-12T05:45:37.994432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
469702
 
0.2%
459342
 
0.2%
376882
 
0.2%
499422
 
0.2%
204422
 
0.2%
56882
 
0.2%
323782
 
0.2%
465311
 
0.1%
480491
 
0.1%
353921
 
0.1%
Other values (983)983
98.3%
ValueCountFrequency (%)
5281
0.1%
6551
0.1%
7131
0.1%
7641
0.1%
8941
0.1%
9361
0.1%
10001
0.1%
10451
0.1%
10961
0.1%
12141
0.1%
ValueCountFrequency (%)
499611
0.1%
499422
0.2%
499341
0.1%
498061
0.1%
497431
0.1%
497301
0.1%
497061
0.1%
496791
0.1%
496531
0.1%
495181
0.1%

Credit_Score
Real number (ℝ)

High correlation 

Distinct462
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean568.325
Minimum300
Maximum849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-12T05:45:38.086052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile324
Q1438.75
median562
Q3713
95-th percentile821.05
Maximum849
Range549
Interquartile range (IQR)274.25

Descriptive statistics

Standard deviation160.75334
Coefficient of variation (CV)0.28285459
Kurtosis-1.2120624
Mean568.325
Median Absolute Deviation (MAD)140
Skewness0.069738381
Sum568325
Variance25841.635
MonotonicityNot monotonic
2025-10-12T05:45:38.181919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7208
 
0.8%
4047
 
0.7%
7166
 
0.6%
4446
 
0.6%
7605
 
0.5%
8115
 
0.5%
3375
 
0.5%
5775
 
0.5%
8425
 
0.5%
3515
 
0.5%
Other values (452)943
94.3%
ValueCountFrequency (%)
3001
 
0.1%
3012
0.2%
3021
 
0.1%
3032
0.2%
3044
0.4%
3062
0.2%
3072
0.2%
3082
0.2%
3091
 
0.1%
3102
0.2%
ValueCountFrequency (%)
8491
 
0.1%
8482
 
0.2%
8471
 
0.1%
8462
 
0.2%
8452
 
0.2%
8444
0.4%
8432
 
0.2%
8425
0.5%
8411
 
0.1%
8401
 
0.1%

Age
Real number (ℝ)

Distinct62
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.514
Minimum18
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-12T05:45:38.278053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q133
median49
Q364
95-th percentile76
Maximum79
Range61
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.573236
Coefficient of variation (CV)0.3622302
Kurtosis-1.1749324
Mean48.514
Median Absolute Deviation (MAD)15.5
Skewness0.0032558033
Sum48514
Variance308.81862
MonotonicityNot monotonic
2025-10-12T05:45:38.377745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3327
 
2.7%
5224
 
2.4%
5024
 
2.4%
3023
 
2.3%
6922
 
2.2%
5121
 
2.1%
7521
 
2.1%
2720
 
2.0%
7220
 
2.0%
5819
 
1.9%
Other values (52)779
77.9%
ValueCountFrequency (%)
1813
1.3%
1917
1.7%
2012
1.2%
2117
1.7%
2211
1.1%
2316
1.6%
2414
1.4%
2518
1.8%
2613
1.3%
2720
2.0%
ValueCountFrequency (%)
7919
1.9%
7810
1.0%
779
0.9%
7616
1.6%
7521
2.1%
7418
1.8%
7312
1.2%
7220
2.0%
7112
1.2%
7014
1.4%

Label_LoanDefault
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
519 
0
481 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1519
51.9%
0481
48.1%

Length

2025-10-12T05:45:38.465250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-12T05:45:38.531886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1519
51.9%
0481
48.1%

Most occurring characters

ValueCountFrequency (%)
1519
51.9%
0481
48.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1519
51.9%
0481
48.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1519
51.9%
0481
48.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1519
51.9%
0481
48.1%

Interactions

2025-10-12T05:45:35.979152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:33.802752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.261075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.681872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:35.119474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:35.554705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:36.058972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:33.886090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.344445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.756949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:35.198494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:35.627764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:36.125658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:33.961094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.410977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.827767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:35.265301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:35.694391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:36.200653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.036099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.473362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.894433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:35.340279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:35.767339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:36.269434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.111086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.544432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.964614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:35.411104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:35.840262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:36.340335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.186185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:34.615256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:35.036636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:35.484032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-12T05:45:35.908965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-10-12T05:45:38.581917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AgeCredit_ScoreFrequencyLabel_LoanDefaultLoan_AmountMonetary_ValueRecency_Days
Age1.0000.026-0.0190.035-0.014-0.0450.010
Credit_Score0.0261.0000.0230.589-0.029-0.034-0.008
Frequency-0.0190.0231.0000.0000.0560.008-0.053
Label_LoanDefault0.0350.5890.0001.0000.0650.0480.000
Loan_Amount-0.014-0.0290.0560.0651.000-0.022-0.002
Monetary_Value-0.045-0.0340.0080.048-0.0221.000-0.013
Recency_Days0.010-0.008-0.0530.000-0.002-0.0131.000

Missing values

2025-10-12T05:45:36.431911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-12T05:45:36.527720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CustomerIDRecency_DaysFrequencyMonetary_ValueLoan_AmountCredit_ScoreAgeLabel_LoanDefault
0CUST000013340879339012474301
1CUST000022823771433756763200
2CUST0000323928403546531319560
3CUST0000416044463648049545601
4CUST000051587799935392740650
5CUST0000631346137718860824760
6CUST000073242439730153438481
7CUST0000825425160821541657691
8CUST000097436789731640404621
9CUST000103526635940700363351
CustomerIDRecency_DaysFrequencyMonetary_ValueLoan_AmountCredit_ScoreAgeLabel_LoanDefault
990CUST0099112527461437846656211
991CUST0099220022641537811587470
992CUST00993721520117442845690
993CUST0099415832857614613603581
994CUST0099519337772825817653260
995CUST009962281634717084416311
996CUST009971922830861764710701
997CUST0099813249354142362750780
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